Apriori Base Efficient Approach for Large Database Compressed in Association Mining

Data mining can be viewed as a result of the natural evolution of information technology. Large Amount of data is being using very rapidly in the world. It to be compressed takes much more time and takes lot of effort to process these data for knowledge discovery and decision making. Data compression technique is one of good solutions to be reduce size of data that can be save more time the time of discovering useful knowledge by using appropriate methods data mining approaches which is used to compress the original database into a smaller one and perform the data mining process for compressed transaction such as M2TQT, APRIORI algorithm, TM algorithm, AIS & SETM, CT-Apriori algorithm, CBMine, CT-ITL algorithm, FIUT- Tree. Among the various techniques M2TQT uses the relationship of transactions to merge related transactions and builds a quantification table to prune the candidate item sets which are impossible to become frequent in order to improve the performance of mining association rules. Thus M2TQT is observed to perform better than existing approaches.